Breast cancer is the second leading cause of cancer death for all women in the world and the most common cause of death from cancer in Australian women. Early detection is the key to successful treatment and reduction of mortality. Mammography is a widely accepted way for breast cancer early detection. However, there are still 10%-30% of women who have breast cancer and undergo mammography have negative mammograms.
A computer aided diagnosis (“CAD”) system can serve as the second reader to aid radiologists in breast cancer detection and diagnosis saving both cost and time. Current image processing techniques make breast abnormality detection easier, however classification of malignant and benign cancers is still a very challenging and a difficult problem for researchers. The key factors affecting the classification results are feature extraction and classification techniques.
Abnormal findings in breast cancer are typically manifested by calcifications and/or masses. Microcalcification is a tiny calcium deposit that has accumulated in the breast tissue, which is an early signature for discriminating between normal and cancerous tissues, and it appears as a small bright spot on the mammogram. Masses can also be an indicator of breast cancer.
The CAD system generally consists of image acquisition, image pre-processing, area segmentation, and feature extraction, followed by classification. Suspicious areas can be located using various techniques, and different classification methods can be used for further breast abnormality classification. The classification is usually based on the features extracted from the suspicious areas. Over the years, researchers have developed many different methods and computer algorithms to improve CAD effectiveness. However, previous researchers have not yet produced a stable and accurate system, and classification of malignant and benign cancers is still a very challenging problem for researchers. Accordingly, there exists a need to provide an improved system and method able to provide results that are better than prior known methods and systems.
The reference to any prior art in this specification is not, and should not be taken as an acknowledgement or any form of suggestion that the referenced prior art forms part of the common general knowledge in Australia.